Developing a Multi-Spectral NIR LED-Based Instrument for the Detection of Pesticide Residues Containing...

Area of Science:

• Agricultural Science
• Analytical Chemistry
• Food Science

Background:

• Lab-based near-infrared (NIR) spectroscopy, like the DA Perten 7200, can detect chlorpyrifos-methyl pesticide residue in rice.
• Current lab-based methods are not suitable for field or point-of-sale testing, limiting rapid food safety assessments.

Purpose of the Study:

• To evaluate the feasibility of a field-deployable, light-emitting diode (LED)-based near-infrared (NIR) instrument for detecting chlorpyrifos-methyl pesticide residue in various rice types.
• To simulate and assess the performance of two distinct LED-based instrument configurations for pesticide residue analysis.

Main Methods:

• Collected NIR reflectance spectra (850-1550 nm) from rough, brown, and milled rice treated with varying chlorpyrifos-methyl concentrations.
• Utilized partial least squares regression (PLS) for quantitative analysis and discriminant analysis (DA) for qualitative classification.
• Simulated two LED-based instruments (LEDPrototype1 and LEDPrototype2) using spectral data from the DA7200, selecting wavelengths based on PLS coefficients and LED availability.

Main Results:

• Simulations for LEDPrototype1 (850-1550 nm) yielded R-squared values from 0.52 to 0.71 and correct classification rates of 70.4% to 100%.
• Simulations for LEDPrototype2 (980-1650 nm) showed R-squared values from 0.59 to 0.82 and correct classification rates of 66% to 100%.
• Both simulated LED-based instruments demonstrated the potential for detecting varying levels of chlorpyrifos-methyl in different rice forms.

Conclusions:

• A multi-spectral LED-based instrument is a viable option for detecting chlorpyrifos-methyl pesticide residue in rough, brown, and milled rice.
• This technology offers a promising pathway towards developing field-deployable devices for rapid food safety monitoring.
• Further development and validation of LED-based instruments could enhance on-site pesticide residue detection capabilities.